class_docstr: Represents the schema of a dataset with tensor features and targets.
code: def __init__(self, features: Schema, targets: Schema = None):
      if not isinstance(features, Schema):
          raise MlflowException(
              f"features must be mlflow.types.Schema, got '{type(features)}'",
              INVALID_PARAMETER_VALUE,
          )
      if targets is not None and not isinstance(targets, Schema):
          raise MlflowException(
              f"targets must be either None or mlflow.types.Schema, got '{type(features)}'",
              INVALID_PARAMETER_VALUE,
          )
      self.features = features
      self.targets = targets
  
file_path: mlflow/mlflow/data/schema.py
imports: from typing import Any, Dict
  from mlflow.exceptions import MlflowException
  from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
  from mlflow.types.schema import Schema
label_desc: Use image node_id 1 to create a new TensorDatasetSchema object  with example: obj = TensorDatasetSchema(features, targets)